Title :
Feature extraction using fuzzy complete linear discriminant analysis
Author :
Cui, Yan ; Jin, Zhong
Author_Institution :
Sch. of Comput. Sci. & Technol., Nanjing Univ. of Sci. & Technol., Nanjing, China
Abstract :
In pattern recognition, feature extraction techniques are widely employed to dimensionality reduction. In this paper, a novel feature extraction method, fuzzy complete linear discriminant analysis (Fuzzy-CLDA), is proposed by combining the complete linear discriminant analysis (CLDA) and the membership degrees of samples. Furthermore, we calculate the sample membership degrees with different distance metrics and compare the effectiveness of the distance metrics. In addition, experiments are provided for analyzing and illustrating our results.
Keywords :
fuzzy set theory; pattern recognition; dimensionality reduction; distance metrics; feature extraction; fuzzy complete linear discriminant analysis; fuzzy-CLDA; pattern recognition; Error analysis; Feature extraction; Linear discriminant analysis; Measurement; Pattern recognition; Principal component analysis; Vectors;
Conference_Titel :
Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on
Conference_Location :
Brisbane, QLD
Print_ISBN :
978-1-4673-1507-4
Electronic_ISBN :
1098-7584
DOI :
10.1109/FUZZ-IEEE.2012.6250813